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Abstract #0750

Multishot high-resolution brain diffusion-weighted imaging using phase regularized reconstruction

Yuxin Hu1,2, Xiaole Wang3, Evan G. Levine1,2, Qiyuan Tian1,2, Valentina Taviani4, Frank Ong5, Shreyas Vasanawala1, Jennifer A McNab1, Bruce L. Daniel1,6, and Brian Hargreaves1,2,6

1Department of Radiology, Stanford University, Stanford, CA, United States, 2Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 3Biomedical Engineering, Tsinghua University, Beijing, China, 4GE Healthcare, Menlo Park, CA, United States, 5Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States, 6Department of Bioengineering, Stanford University, Stanford, CA, United States

Multishot imaging has been shown to provide high resolution diffusion-weighted images (DWIs) with reduced distortion, however, significant aliasing artifacts and signal cancellation still occur due to the mismatch of the motion-induced phase between different shots. The reconstruction becomes non-convex and intractable to solve when this phase is included into the forward model. The goal of this work is to solve this problem by circumventing the challenging phase estimation step. The brain and breast examples demonstrate that this can be efficiently achieved using a locally low-rank reconstruction approach.

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